Population Pharmacokinetics of Topiramate in Patients with Epilepsy Using Nonparametric Modeling.

BACKGROUND: Topiramate (TPM) is used for the treatment of various epileptic seizures and the prevention of migraine. This study aimed to develop a population pharmacokinetic model and identify covariates that influence TPM behavior in patients with epilepsy in Kuwait. METHODS: Data were collected retrospectively from 108 patients (2 years old and above) with epilepsy who were treated with oral TPM and 174 TPM blood samples from 3 hospitals in Kuwait from 2009 to 2016. Data were randomly divided into 2 groups for model development and validation. The population pharmacokinetic model was built using the nonparametric modeling algorithm (Pmetrics). The model was evaluated internally through the visual predictive check method and externally using a new data set. RESULTS: A 1-compartment model with first-order elimination fitted the data well. Covariates showing a significant effect on the elimination rate constant were renal function and coadministration of carbamazepine (CBZ). The mean estimated clearance was 2.11 L/h; this was 50% higher for patients coadministered with CBZ. Age and sex were essential covariates for the volume of distribution (V). The visual predictive check of the final model could predict the measured concentrations. External validation further confirmed the favorable predictive performance of the model with low bias and imprecision for predicting the concentration in a particular population. CONCLUSIONS: TPM elimination was increased with CBZ coadministration and was affected by renal function. Meanwhile, age and sex were the main predictors for V. The predictive performance of the final model proved to be valid internally and externally.

Amikacin population pharmacokinetics in critically ill Kuwaiti patients.

Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm. The estimations of the developed model were assessed using mean error (ME) as a measure of bias and mean squared error (MSE) as a measure of precision. A total of 331 serum amikacin concentrations were obtained from 56 patients. The mean (± SD) model parameter values found were Vc = 0.2302 ± 0.0866 L/kg, kslope = 0.004045 ± 0.00705 min per unit of creatinine clearance, k12 = 2.2121 ± 5.506 h(-1), and k21 = 1.431 ± 2.796 h(-1). The serum concentration data were estimated with little bias (ME = -0.88) and good precision (MSE = 13.08). The present study suggests that amikacin pharmacokinetics in adult Kuwaiti ICU patients are generally rather similar to those found in other patients. This population model would provide useful guidance in developing initial amikacin dosage regimens for such patients, especially using multiple model (MM) dosage design, followed by appropriate Bayesian adaptive control, to optimize amikacin dosage regimens for each individual patient.

Clinical pharmacokinetics of gentamicin. Estimation of initial dosing parameters in hospitalized patients at Al-Amiri Hospital Kuwait.

OBJECTIVES: The objectives of this study were to: (1) derive equations for estimating gentamicin clearance (Cl(gent)) and volume of distribution (V(d)) based on the local population attending Al-Amiri Hospital, Kuwait; (2) independently evaluate these equations by comparison with other published methods in their predictive ability to estimate Cl(gent) and V(d). MATERIALS AND METHODS: Cl(gent) and V(d) were calculated in 47 patients (group 1) using the Sawchuk-Zaske method. Regression analysis was used to derive a correlation between creatinine clearance (Cl(cr)) and Cl(gent), V(d) and actual body weight (ABW). Based on actual Cl(gent) and V(d) values, the predictive ability of the estimated parameters from the regression equations was validated and compared with 4 published methods using mean error (ME), i.e. bias, and mean squared error (MSE) and root mean squared error (RMSE), i.e. precision. All equations were also evaluated in an independent second group (group 2) of 23 patients. RESULTS: The mean +/- SD values of Cl(gent) and V(d) were 4.0 +/- 1.8 l x h(-1) and 16.8 +/- 6.7 liters, respectively. The derived equations were: Cl(gent) = (0.760) (Cl(cr)) + 1.117 (r = 0.701) and V(d) = (0.165) (ABW) + 5.604 (r = 0.532). In comparison to the 4 published methods, the derived equations were less biased (ME = 0.00) and more precise (MSE = 1.68, RMSE = 1.02) in predicting Cl(gent) (p < 0.05), and less biased (ME = -0.01) with no difference in precision (MSE = 36.22, RMSE = 4.59) in predicting V(d) (p > 0.05). This precision was confirmed in the second group of 23 patients, where the derived equations were less biased (ME = -0.1) and more precise (MSE = 3.22, RMSE = 1.48) in predicting Cl(gent) (p < 0.05), whilst no difference was found for prediction of V(d) (p > 0.05). CONCLUSION: The equations developed in this study provided a reliable estimation of Cl(gent) and V(d). It is planned to use them at Kuwait Hospitals to help provide more individualized patient dosing information to physicians.

Therapeutic drug monitoring of gentamicin: evaluation of five nomograms for initial dosing at Al-Amiri Hospital in Kuwait.

OBJECTIVE: To compare five published nomograms (Thomson guidelines, Mawer nomogram, rule of eights, Hull-Sarubbi table and Dettli method) for calculating the initial gentamicin dosage regimen in a Kuwaiti population. MATERIALS AND METHODS: Based on measured peak and trough gentamicin concentrations, the elimination rate constant and volume of distribution of gentamicin were calculated for each patient (n = 56), using a modified two-point Sawchuk-Zaske method. The calculated individual set of pharmacokinetic parameters and the initial dose regimen recommended by each of the five methods were used to predict the steady-state peak and trough of gentamicin concentrations. RESULTS: The Thomson guidelines produced consistent results in predicting gentamicin concentrations within the target ranges of peak plus trough, peak only and trough only (63, 75 and 75%, respectively). The Mawer nomogram, Hull-Sarubbi table and Dettli methods achieved similar percentages of patients (46-50%) within the target ranges (5-10 mg x l(-1) for peak and 0.5-2 for trough), whereas empirical dosing and the rule of eights showed the lowest percentages of patients within the peak plus trough target range (25 and 37%, respectively). However, with respect to the underdosing target range (predicted concentration <5 mg x l(-1)), the Thomson guidelines showed that 21% of patients were underdosed. CONCLUSION: The results show that a large number of patients (37-63%) were outside the target ranges in all initial gentamicin dosing methods evaluated in this study. Therefore, serum concentration measurement can be advised to assist in the optimization of gentamicin dose selection.

Therapeutic drug monitoring of high-dose gentamicin in an elderly patient: a case report.

OBJECTIVE: To report the pharmacokinetics of gentamicin using traditional multiple daily doses and a high-dose regimen in an elderly patient. CLINICAL PRESENTATION AND INTERVENTION: An 80-year-old male who presented with mild renal failure received two different gentamicin dosing regimens, 60 mg every 8 h for septicemia and a high dose of 400 mg with extended interval for suspected endocarditis. Based on population parameters of k(e) (0.1030 h(-1)) and Vd (18.1 liters), the initial gentamicin dosage regimen was calculated to be 80 mg every 12 h. The measured peak and trough concentrations were used to calculate the individual parameters of k(e) (0.0749 h(-1)) and Vd (30.9 liters). After a 5-mg.kg(-1) gentamicin dose, the Hartford nomogram was used to estimate the extended dosage interval. CONCLUSION: The Hartford nomogram may be a valid tool for estimating the dosage interval after a 5-mg.kg(-1) single dose of gentamicin.